Liuhaotian
Models by this creator
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llava-v1.5-13b
428
llava-v1.5-13b is an open-source chatbot trained by fine-tuning LLaMA and Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The model was trained and released by liuhaotian, a prominent AI researcher. Similar models include the smaller llava-v1.5-7b, the fine-tuned llava-v1.5-7B-GGUF, and the LLaVA-13b-delta-v0 delta model. Model inputs and outputs llava-v1.5-13b is a multimodal language model that can process both text and images. It takes in a prompt containing both text and the `` tag, and generates relevant text output in response. Inputs Text prompt containing the `` tag One or more images Outputs Relevant text output generated in response to the input prompt and image(s) Capabilities llava-v1.5-13b excels at tasks involving multimodal understanding and instruction-following. It can answer questions about images, generate image captions, and perform complex reasoning over both text and visual inputs. The model has been evaluated on a variety of benchmarks, including academic VQA datasets and recent instruction-following datasets, and has demonstrated strong performance. What can I use it for? The primary intended uses of llava-v1.5-13b are research on large multimodal models and chatbots. Researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence can use the model to explore and develop new techniques in these domains. The model's capabilities in multimodal understanding and instruction-following make it a valuable tool for applications such as visual question answering, image captioning, and interactive AI assistants. Things to try One interesting aspect of llava-v1.5-13b is its ability to handle multiple images and prompts simultaneously. Users can experiment with providing the model with a prompt that references several images and see how it generates responses that integrate information from the different visual inputs. Additionally, the model's strong performance on instruction-following tasks suggests opportunities for exploring interactive, task-oriented applications that leverage its understanding of natural language and visual cues.
Updated 5/28/2024
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llava-v1.6-34b
275
The llava-v1.6-34b is an open-source chatbot developed by liuhaotian that is trained by fine-tuning a large language model (LLM) on multimodal instruction-following data. It is based on the transformer architecture and uses the NousResearch/Nous-Hermes-2-Yi-34B as its base LLM. The model is part of the LLaVA family, which includes similar versions like llava-v1.5-13b, llava-v1.5-7b, llava-v1.6-mistral-7b, and LLaVA-13b-delta-v0. These models differ in their base LLM, training dataset, and model size. Model inputs and outputs Inputs The model accepts natural language instructions and prompts as input. It can also accept image data as input for multimodal tasks. Outputs The model generates human-like responses in natural language. For multimodal tasks, the model can generate relevant images as output. Capabilities The llava-v1.6-34b model has been trained to engage in a wide range of tasks, including natural language processing, computer vision, and multimodal reasoning. It has shown strong performance on tasks such as answering complex questions, following detailed instructions, and generating relevant images. What can I use it for? The primary use of the llava-v1.6-34b model is for research on large multimodal models and chatbots. It can be particularly useful for researchers and hobbyists working in computer vision, natural language processing, machine learning, and artificial intelligence. Some potential use cases for the model include: Building chatbots and virtual assistants with multimodal capabilities Developing visual question answering systems Exploring new techniques for instruction-following in language models Advancing research on multimodal reasoning and understanding Things to try One interesting aspect of the llava-v1.6-34b model is its ability to combine text and image data to perform complex tasks. Researchers could experiment with using the model to generate images based on textual descriptions, or to answer questions that require both visual and linguistic understanding. Another area to explore is the model's performance on tasks that require strong reasoning and problem-solving skills, such as scientific question answering or task-oriented dialogue. By probing the model's capabilities in these areas, researchers can gain valuable insights into the strengths and limitations of large multimodal language models.
Updated 5/27/2024
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llava-v1.5-7b
274
llava-v1.5-7b is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model based on the transformer architecture. The model was created by liuhaotian, and similar models include llava-v1.5-7B-GGUF, LLaVA-13b-delta-v0, llava-v1.6-mistral-7b, and llava-1.5-7b-hf. Model inputs and outputs llava-v1.5-7b is a large language model that can take in textual prompts and generate relevant responses. The model is particularly designed for multimodal tasks, allowing it to process and generate text based on provided images. Inputs Textual prompts in the format "USER: \nASSISTANT:" Optional image data, indicated by the `` token in the prompt Outputs Generated text responses relevant to the given prompt and image (if provided) Capabilities llava-v1.5-7b can perform a variety of tasks, including: Open-ended conversation Answering questions about images Generating captions for images Providing detailed descriptions of scenes and objects Assisting with creative writing and ideation The model's multimodal capabilities allow it to understand and generate text based on both textual and visual inputs. What can I use it for? llava-v1.5-7b can be a powerful tool for researchers and hobbyists working on projects related to computer vision, natural language processing, and artificial intelligence. Some potential use cases include: Building interactive chatbots and virtual assistants Developing image captioning and visual question answering systems Enhancing text generation models with multimodal understanding Exploring the intersection of language and vision in AI By leveraging the model's capabilities, you can create innovative applications that combine language and visual understanding. Things to try One interesting thing to try with llava-v1.5-7b is its ability to handle multi-image and multi-prompt generation. This means you can provide multiple images in a single prompt and the model will generate a response that considers all the visual inputs. This can be particularly useful for tasks like visual reasoning or complex scene descriptions. Another intriguing aspect of the model is its potential for synergy with other large language models, such as GPT-4. As mentioned in the LLaVA-13b-delta-v0 model card, the combination of llava-v1.5-7b and GPT-4 set a new state-of-the-art on the ScienceQA dataset. Exploring these types of model combinations and their capabilities can lead to exciting advancements in the field of multimodal AI.
Updated 5/28/2024
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LLaVA-13b-delta-v0
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The LLaVA-13b-delta-v0 model is an open-source chatbot trained by fine-tuning the LLaMA language model and Vicuna on GPT-generated multimodal instruction-following data. It is an autoregressive language model based on the transformer architecture. The model was developed by liuhaotian, who has also created similar models such as llava-v1.6-mistral-7b and llava-med-7b-delta. Model Inputs and Outputs The LLaVA-13b-delta-v0 model is a language model that can generate human-like text given a prompt. It also has multimodal capabilities, allowing it to generate text based on both textual and visual inputs. Inputs Text prompts**: The model can accept text prompts to generate relevant responses. Images**: The model can also accept images as part of the input, allowing it to generate text describing or relating to the provided image. Outputs Textual responses**: The primary output of the model is human-like textual responses to the provided prompts or image-text combinations. Capabilities The LLaVA-13b-delta-v0 model has been trained to engage in open-ended conversation, answer questions, and describe images. It demonstrates strong language understanding and generation capabilities, as well as the ability to reason about and describe visual information. The model can be particularly useful for research on large multimodal models and chatbots. What Can I Use It For? The primary intended use of the LLaVA-13b-delta-v0 model is for research on large multimodal models and chatbots. Researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence may find this model useful for exploring various multimodal applications and advancing the state of the art in these fields. Things to Try Some interesting things to try with the LLaVA-13b-delta-v0 model include: Evaluating the model's ability to understand and describe complex visual scenes by providing it with a diverse set of images. Exploring the model's language understanding and generation capabilities by engaging it in open-ended conversations on a variety of topics. Investigating the model's reasoning abilities by asking it to answer questions that require combining information from both text and visual inputs. Experimenting with different prompting strategies to see how the model's responses can be tailored for specific use cases or applications.
Updated 5/27/2024
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llava-v1.6-mistral-7b
194
The llava-v1.6-mistral-7b is an open-source chatbot model developed by Haotian Liu that combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. It is an auto-regressive language model based on the transformer architecture, fine-tuned on a diverse dataset of image-text pairs and multimodal instruction-following data. The model builds upon the Mistral-7B-Instruct-v0.2 base model, which provides improved commercial licensing and bilingual support compared to earlier versions. Additionally, the training dataset for llava-v1.6-mistral-7b has been expanded to include more diverse and high-quality data, as well as support for dynamic high-resolution image input. Similar models include the llava-v1.6-mistral-7b-hf and llava-1.5-7b-hf checkpoints, which offer slightly different model configurations and training datasets. Model inputs and outputs Inputs Text prompt**: The model takes a text prompt as input, which can include instructions, questions, or other natural language text. Image**: The model can also take an image as input, which is integrated into the text prompt using the `` token. Outputs Text response**: The model generates a relevant text response to the input prompt, in an auto-regressive manner. Capabilities The llava-v1.6-mistral-7b model is capable of handling a variety of multimodal tasks, such as image captioning, visual question answering, and open-ended dialogue. It can understand and reason about the content of images, and generate coherent and contextually appropriate responses. What can I use it for? You can use the llava-v1.6-mistral-7b model for research on large multimodal models and chatbots, or for building practical applications that require visual understanding and language generation, such as intelligent virtual assistants, image-based search, or interactive educational tools. Things to try One interesting aspect of the llava-v1.6-mistral-7b model is its ability to handle dynamic high-resolution image input. You could experiment with providing higher-quality images to the model and observe how it affects the quality and level of detail in the generated responses. Additionally, you could explore the model's performance on specialized benchmarks for instruction-following language models, such as the collection of 12 benchmarks mentioned in the model description, to better understand its strengths and limitations in this domain.
Updated 5/28/2024
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llava-v1.6-vicuna-7b
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llava-v1.6-vicuna-7b is an open-source chatbot model developed by liuhaotian. It is a large language model (LLM) based on the Transformer architecture, trained by fine-tuning the lmsys/vicuna-7b-v1.5 model on a diverse multimodal dataset. Similar models include the llava-v1.5-7b, llava-v1.5-13b, llava-v1.6-34b, llava-v1.5-7B-GGUF, and llava-v1.6-mistral-7b models, also developed by liuhaotian and his team. Model inputs and outputs llava-v1.6-vicuna-7b is a text-to-text model, taking natural language input and generating coherent text responses. The model is trained on a variety of datasets, including image-text pairs, multimodal instruction-following data, academic VQA tasks, and conversational data. This gives the model broad capabilities to engage in open-ended dialogue, answer questions, and follow instructions across different domains. Inputs Natural language text prompts Multimodal inputs like images (when combined with text) Outputs Coherent text responses Answers to questions Completion of instructions Capabilities llava-v1.6-vicuna-7b demonstrates strong performance on a range of language tasks, including open-ended conversation, question answering, and task completion. The model can engage in fluent dialogue, provide informative responses, and follow multi-step instructions. It also exhibits some multimodal capabilities, allowing it to interpret and reason about visual information when paired with text. What can I use it for? The primary intended uses of llava-v1.6-vicuna-7b are research on large multimodal models and chatbots. The model can be used by researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence to explore the capabilities and limitations of such systems. Potential applications include virtual assistants, content generation, and task automation, though the model is not intended for commercial use. Things to try Experiment with llava-v1.6-vicuna-7b to see how it handles open-ended dialogue, question answering, and instruction following across different domains. Try providing the model with multimodal inputs, such as images paired with text, to see how it can leverage visual information. Explore the model's strengths and weaknesses, and compare its performance to similar models like the llava-v1.5-7b or llava-v1.6-mistral-7b.
Updated 5/30/2024
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LLaVA-Lightning-MPT-7B-preview
50
LLaVA-Lightning-MPT-7B-preview is a research preview of the LLaVA model, which is an open-source chatbot trained by fine-tuning the LLaMA/Vicuna/MPT language models on GPT-generated multimodal instruction-following data. This model is based on the MPT-7B-chat checkpoint and can be used directly without needing to apply delta weights. Unlike other LLaVA models, this preview version does not require the additional conversion step. The primary use of LLaVA is research on large multimodal models and chatbots, with the target audience being researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. Model inputs and outputs LLaVA-Lightning-MPT-7B-preview is an auto-regressive language model that can engage in multimodal tasks. It takes in a combination of text and visual inputs and generates relevant text outputs. Inputs Text prompts for conversational, detailed description, and complex reasoning tasks Images associated with the prompts Outputs Textual responses that demonstrate the model's understanding and reasoning about the provided inputs Capabilities LLaVA-Lightning-MPT-7B-preview has been evaluated on a set of 90 visual reasoning questions, where it demonstrated strong performance in conversational, detailed description, and complex reasoning tasks. The model has also been evaluated on the ScienceQA dataset, where it achieved state-of-the-art results in synergy with GPT-4. What can I use it for? The primary intended use of LLaVA-Lightning-MPT-7B-preview is for research on large multimodal models and chatbots. Researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence can explore the model's capabilities and use it as a testbed for further advancements in these fields. Things to try Researchers can experiment with fine-tuning the LLaVA-Lightning-MPT-7B-preview model on specific datasets or tasks to explore its adaptability and performance. Additionally, users can compare the model's behavior and outputs with other similar models, such as LLaVA-13b-delta-v0 and llava-v1.5-7b, to gain a deeper understanding of the model's strengths and limitations.
Updated 5/28/2024